The effects of peripheral nerve impairments on postural control and mobility among people with peripheral neuropathy

Approximately 20 million Americans are suffering Peripheral Neuropathy (PN). It is estimated that the prevalence of all-cause PN is about 2.4% in the entire adult population, whereas over 8-10% in the population segment over the age of 55 (Martyn & Hughes, 1997). Peripheral Neuropathy leads to a high risk of falling, resulting from the deficits of postural control caused by the impaired peripheral nerves, especially the degenerative somatosensory system. To date, there is no effective medical treatment for the disease but pain managements. The deficits of postural control decrease the life quality of this population. The degeneration of peripheral nerves reduces sensory inputs from the somatosensory system to central nervous system via spinal reflexive loop, which should provide valuable real-time information for balance correction. Therefore, it is necessary to investigate how PN affects the somatosensory system regarding postural control. Besides that, people with PN may develop a compensatory mechanism which could be reinforced by exercise training, ultimately to improve balance and mobility in their daily life. The neuroplasticity may occur within somatosensory system by relying on relative intact sensory resources. Hence, unveiling the compensatory mechanism in people with PN may help in understanding (a) essential sensations or function of peripheral nerves to postural control, (b) effective strategy of physical treatments for people with PN, and (c) task-dependent sensory information requirements. Therefore, this dissertation discussed the roles of foot sole sensation, ankle proprioception, and stretch reflex on balance as well as gait among people with PN. Furthermore, the discussion of the coupling between small and large afferent reflexive loops may spot the compensatory mechanism in people with PN

Relating reflex gain modulation in posture control to underlying neural network properties using a neuromusculoskeletal model

During posture control, reflexive feedback allows humans to efficiently compensate for unpredictable mechanical disturbances. Although reflexes are involuntary, humans can adapt their reflexive settings to the characteristics of the disturbances. Reflex modulation is commonly studied by determining reflex gains: a set of parameters that quantify the contributions of Ia, Ib and II afferents to mechanical joint behavior. Many mechanisms, like presynaptic inhibition and fusimotor drive, can account for reflex gain modulations. The goal of this study was to investigate the effects of underlying neural and sensory mechanisms on mechanical joint behavior. A neuromusculoskeletal model was built, in which a pair of muscles actuated a limb, while being controlled by a model of 2,298 spiking neurons in six pairs of spinal populations. Identical to experiments, the endpoint of the limb was disturbed with force perturbations. System identification was used to quantify the control behavior with reflex gains. A sensitivity analysis was then performed on the neuromusculoskeletal model, determining the influence of the neural, sensory and synaptic parameters on the joint dynamics. The results showed that the lumped reflex gains positively correlate to their most direct neural substrates: the velocity gain with Ia afferent velocity feedback, the positional gain with muscle stretch over II afferents and the force feedback gain with Ib afferent feedback. However, position feedback and force feedback gains show strong interactions with other neural and sensory properties. These results give important insights in the effects of neural properties on joint dynamics and in the identifiability of reflex gains in experiments

Analysis of control strategies for VIVA OpenHBM with active refexive neck muscles

Modeling muscle activity in the neck muscles of a finite element (FE) human body model can be based on two biological reflex systems. One approach is to approximate the Vestibulocollic reflex (VCR) function, which maintains the head orientation relative to a fixed reference in space. The second system tries to maintain the head posture relative to the torso, similar to the Cervicocolic reflex (CCR). Strategies to combine these two neck muscle controller approaches in a single head-neck FE model were tested, optimized, and compared to rear-impact volunteer data. The first approach, Combined-Control, assumed that both controllers simultaneously controlled all neck muscle activations. In the second approach, Distributed-Control, one controller was used to regulate activation of the superficial muscles while a different controller acted on deep neck muscles. The results showed that any muscle controller that combined the two approaches was less effective than only using one of VCR- or CCR-based systems on its own. A passive model had the best objective rating for cervical spine kinematics, but the addition of a single active controller provided the best response for both head and cervical spine kinematics. The present study demonstrates the difficulty in completely capturing representative head and cervical spine responses to rear-impactloading and identified a controller capturing the VCR reflex as the best candidate to investigate whiplash injury mechanisms through FE modeling

The Contribution of Small and Large Sensory Afferents to Postural Control in Patients With Peripheral Neuropathy

Peripheral neuropathy (PN) is a multifarious disorder that is caused by damage to the peripheral nerves. Although the symptoms of PN vary with the etiology, most cases are characterized by impaired tactile and proprioceptive sensation that progresses in a distal to proximal manner. Balance also tends to deteriorate as the disorder becomes more severe, and those afflicted are substantially more likely to fall while walking compared with those who are healthy. Most patients with PN walk more cautiously and with greater stride variability than age-matched controls, but the majority of their falls occur when they must react to a perturbation such as a slippery or uneven surface. The purpose of this study was to first describe the role of somatosensory feedback in the control of posture and then discuss how that relationship is typically affected by the most common types of PN. A comprehensive review of the scientific literature was conducted using MEDLINE, and the relevant information was synthesized. The evidence indicates that the proprioceptive feedback that is conveyed primarily through larger type I afferents is important for postural control. However, the evidence indicates that the tactile feedback communicated through smaller type II afferents is particularly critical to the maintenance of balance. Many forms of PN often lead to chronic tactile desensitization in the soles of the feet and, although the central nervous system seems to adapt to this smaller type II afferent dysfunction by relying on more larger type I afferent reflex loops, the result is still decreased stability. We propose a model that is intended both to help explain the relationship between stability and the smaller type II afferent and the larger type I afferent feedback that may be impaired by PN and to assist in the development of pertinent rehabilitative interventions

Evaluation of motion comfort using advanced active human body models and efficient simplified models

Active muscles are crucial for maintaining postural stability when seated in a moving vehicle. Advanced active 3D non-linear full body models have been developed for impact and comfort simulation, including large numbers of individual muscle elements, and detailed non-linear models of the joint structures. While such models have an apparent potential to provide insight into postural stabilization, they are computationally demanding, making them less practical in particular for driving comfort where long time periods are to be studied. In vibrational comfort and in general biomechanical research, linearized models are effectively used. This paper evaluates the effectiveness of simplified 3D full-body human models to capture comfort provoked by whole-body vibrations. An efficient seated human body model is developed and validated using experimental data. We evaluate the required complexity in terms of joints and degrees of freedom for the spine, and explore how well linear spring-damper models can approximate reflexive postural stabilization. Results indicate that linear stiffness and damping models can well capture the human response. The results are improved by adding proportional integral derivative (PID) and head-in-space (HIS) controllers to maintain the defined initial body posture. The integrator is shown to be essential to prevent drift from the defined posture. The joint angular relative displacement is used as the input reference to each PID controller. With this model, a faster than real-time solution is obtained when used with a simple seat model. The paper also discusses the advantages and disadvantages of various models and provides insight into which models are more appropriate for motion comfort analysis

Identification of the contribution of the ankle and hip joints to multi-segmental balance control

Background\ud \ud Human stance involves multiple segments, including the legs and trunk, and requires coordinated actions of both. A novel method was developed that reliably estimates the contribution of the left and right leg (i.e., the ankle and hip joints) to the balance control of individual subjects. \ud \ud Methods\ud \ud The method was evaluated using simulations of a double-inverted pendulum model and the applicability was demonstrated with an experiment with seven healthy and one Parkinsonian participant. Model simulations indicated that two perturbations are required to reliably estimate the dynamics of a double-inverted pendulum balance control system. In the experiment, two multisine perturbation signals were applied simultaneously. The balance control system dynamic behaviour of the participants was estimated by Frequency Response Functions (FRFs), which relate ankle and hip joint angles to joint torques, using a multivariate closed-loop system identification technique. \ud \ud Results\ud \ud In the model simulations, the FRFs were reliably estimated, also in the presence of realistic levels of noise. In the experiment, the participants responded consistently to the perturbations, indicated by low noise-to-signal ratios of the ankle angle (0.24), hip angle (0.28), ankle torque (0.07), and hip torque (0.33). The developed method could detect that the Parkinson patient controlled his balance asymmetrically, that is, the right ankle and hip joints produced more corrective torque. \ud \ud Conclusion\ud \ud The method allows for a reliable estimate of the multisegmental feedback mechanism that stabilizes stance, of individual participants and of separate leg

Cortical Networks for Control of Voluntary Arm Movements Under Variable Force Conditions

A neural model of voluntary movement and proprioception functionally interprets and simulates cell types in movement related areas of primate cortex. The model circuit maintains accurate proprioception while controlling voluntary reaches to spatial targets, exertion of force against obstacles, posture maintenance despite perturbations, compliance with an imposed movement, and static and inertial load compensations. Computer simulations show that model cell properties mimic cell properties in areas 4 and 5. These include delay period activation, response profiles during movement, kinematic and kinetic sensitivities, and latency of activity onset. Model area 4 phasic and tonic cells compute velocity and position commands which activate alpha and gamma motor neurons, thereby shifting the mechanical equilibrium point. Anterior area 5 cells compute limb position using corollary discharges from area 4 and muscle spindle feedback. Posterior area 5 cells use the perceived position and target position signals to compute a desired movement vector. The cortical loop is closed by a volition-gated projection of this movement vector to area 4 phasic cells. Phasic-tonic cells in area 4 incorporate force command components to compensate for static and inertial loads. Predictions are made for both motor and parietal cell types under novel experimental protocols.Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N00014-95-l-0409, N00014-92-J-4015); National Science Foundation (IRI-90-24877, IRI-90-00530

Postural Control and Sensorimotor Integration

Presents state-of-the-art manual therapy research from the last 10 years Multidisciplinary authorship presents the viewpoints of different professions crucial to the ongoing back pain management debate Highly illustrated and fully ..

Effect of Aging on Human Postural Control and the Interaction with Attention

The ability to stand upright and walk is generally taken for granted, yet control of balance utilizes many processes involving the neuromuscular and sensory systems. As we age, balance function begins to decline and can become problematic for many older adults. In particular, adults 65 years of age and older exhibit a higher incidence of falls than younger adults, and falls are a leading cause of injury in older adults, contributing to significant medical costs. Without better understanding of the impact of aging on balance and means to ameliorate those effects, this problem is expected to grow as life expectancy continues to increase.In addition to sensori-motor declines with age that impact balance, another factor known to affect balance, particularly in older adults, is attention, meaning the amount of cognitive resources utilized for a particular task. When two or more tasks vie for cognitive resources, performance in one or more tasks can be compromised (a common example today is driving while talking on a cell phone). Attention has been observed to be a critical factor in many falls reported by older adults. However, it is still not fully understood how aging and attentional demand affect balance and how they interact with each other.In this dissertation, we conducted dual-task experiments and model-based analyses to study upright standing and the interaction of the effects of age and attention on postural control. The effect of age was investigated by testing two age groups (young and older adults) with no evident balance and cognitive impairment and by comparing results of the two groups. The effect of attention and its interaction with age was studied by comparing body sway in the two age groups in response to a moving platform, while either concurrently performing a cognitive task (dual-task) or not (single-task). Our findings highlight postural control differences between young and older adults, as quantified by experimental measures of body motion as well as by model parameter values, such as stiffness, damping and processing delay